Overview

Dataset statistics

Number of variables16
Number of observations730
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory91.4 KiB
Average record size in memory128.2 B

Variable types

Numeric10
Categorical6

Alerts

dteday has a high cardinality: 730 distinct valuesHigh cardinality
instant is highly overall correlated with registered and 3 other fieldsHigh correlation
mnth is highly overall correlated with seasonHigh correlation
weekday is highly overall correlated with workingdayHigh correlation
temp is highly overall correlated with atemp and 4 other fieldsHigh correlation
atemp is highly overall correlated with temp and 4 other fieldsHigh correlation
hum is highly overall correlated with weathersitHigh correlation
casual is highly overall correlated with temp and 4 other fieldsHigh correlation
registered is highly overall correlated with instant and 5 other fieldsHigh correlation
cnt is highly overall correlated with instant and 5 other fieldsHigh correlation
season is highly overall correlated with instant and 3 other fieldsHigh correlation
yr is highly overall correlated with instant and 2 other fieldsHigh correlation
workingday is highly overall correlated with weekday and 1 other fieldsHigh correlation
weathersit is highly overall correlated with humHigh correlation
holiday is highly imbalanced (81.2%)Imbalance
instant is uniformly distributedUniform
dteday is uniformly distributedUniform
yr is uniformly distributedUniform
instant has unique valuesUnique
dteday has unique valuesUnique
weekday has 105 (14.4%) zerosZeros

Reproduction

Analysis started2023-06-13 11:03:29.170855
Analysis finished2023-06-13 11:03:41.956540
Duration12.79 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

instant
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct730
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean365.5
Minimum1
Maximum730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-06-13T16:33:42.347984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile37.45
Q1183.25
median365.5
Q3547.75
95-th percentile693.55
Maximum730
Range729
Interquartile range (IQR)364.5

Descriptive statistics

Standard deviation210.87714
Coefficient of variation (CV)0.57695523
Kurtosis-1.2
Mean365.5
Median Absolute Deviation (MAD)182.5
Skewness0
Sum266815
Variance44469.167
MonotonicityStrictly increasing
2023-06-13T16:33:42.478439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
480 1
 
0.1%
482 1
 
0.1%
483 1
 
0.1%
484 1
 
0.1%
485 1
 
0.1%
486 1
 
0.1%
487 1
 
0.1%
488 1
 
0.1%
489 1
 
0.1%
Other values (720) 720
98.6%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
730 1
0.1%
729 1
0.1%
728 1
0.1%
727 1
0.1%
726 1
0.1%
725 1
0.1%
724 1
0.1%
723 1
0.1%
722 1
0.1%
721 1
0.1%

dteday
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct730
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
01-01-2018
 
1
25-04-2019
 
1
27-04-2019
 
1
28-04-2019
 
1
29-04-2019
 
1
Other values (725)
725 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters7300
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique730 ?
Unique (%)100.0%

Sample

1st row01-01-2018
2nd row02-01-2018
3rd row03-01-2018
4th row04-01-2018
5th row05-01-2018

Common Values

ValueCountFrequency (%)
01-01-2018 1
 
0.1%
25-04-2019 1
 
0.1%
27-04-2019 1
 
0.1%
28-04-2019 1
 
0.1%
29-04-2019 1
 
0.1%
30-04-2019 1
 
0.1%
01-05-2019 1
 
0.1%
02-05-2019 1
 
0.1%
03-05-2019 1
 
0.1%
04-05-2019 1
 
0.1%
Other values (720) 720
98.6%

Length

2023-06-13T16:33:42.592968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01-01-2018 1
 
0.1%
25-01-2018 1
 
0.1%
31-03-2018 1
 
0.1%
22-01-2018 1
 
0.1%
03-01-2018 1
 
0.1%
04-01-2018 1
 
0.1%
05-01-2018 1
 
0.1%
06-01-2018 1
 
0.1%
07-01-2018 1
 
0.1%
08-01-2018 1
 
0.1%
Other values (720) 720
98.6%

Most occurring characters

ValueCountFrequency (%)
0 1624
22.2%
- 1460
20.0%
1 1362
18.7%
2 1158
15.9%
8 499
 
6.8%
9 495
 
6.8%
3 170
 
2.3%
5 134
 
1.8%
7 134
 
1.8%
4 132
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5840
80.0%
Dash Punctuation 1460
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1624
27.8%
1 1362
23.3%
2 1158
19.8%
8 499
 
8.5%
9 495
 
8.5%
3 170
 
2.9%
5 134
 
2.3%
7 134
 
2.3%
4 132
 
2.3%
6 132
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 1460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1624
22.2%
- 1460
20.0%
1 1362
18.7%
2 1158
15.9%
8 499
 
6.8%
9 495
 
6.8%
3 170
 
2.3%
5 134
 
1.8%
7 134
 
1.8%
4 132
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1624
22.2%
- 1460
20.0%
1 1362
18.7%
2 1158
15.9%
8 499
 
6.8%
9 495
 
6.8%
3 170
 
2.3%
5 134
 
1.8%
7 134
 
1.8%
4 132
 
1.8%

season
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
3
188 
2
184 
1
180 
4
178 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters730
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 188
25.8%
2 184
25.2%
1 180
24.7%
4 178
24.4%

Length

2023-06-13T16:33:42.674694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:33:42.805594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
3 188
25.8%
2 184
25.2%
1 180
24.7%
4 178
24.4%

Most occurring characters

ValueCountFrequency (%)
3 188
25.8%
2 184
25.2%
1 180
24.7%
4 178
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 730
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 188
25.8%
2 184
25.2%
1 180
24.7%
4 178
24.4%

Most occurring scripts

ValueCountFrequency (%)
Common 730
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 188
25.8%
2 184
25.2%
1 180
24.7%
4 178
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 188
25.8%
2 184
25.2%
1 180
24.7%
4 178
24.4%

yr
Categorical

HIGH CORRELATION  UNIFORM 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
0
365 
1
365 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters730
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 365
50.0%
1 365
50.0%

Length

2023-06-13T16:33:42.920290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:33:43.061811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 365
50.0%
1 365
50.0%

Most occurring characters

ValueCountFrequency (%)
0 365
50.0%
1 365
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 730
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 365
50.0%
1 365
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 730
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 365
50.0%
1 365
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 365
50.0%
1 365
50.0%

mnth
Real number (ℝ)

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5260274
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-06-13T16:33:43.165780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4502153
Coefficient of variation (CV)0.52868538
Kurtosis-1.207096
Mean6.5260274
Median Absolute Deviation (MAD)3
Skewness-0.010477947
Sum4764
Variance11.903986
MonotonicityNot monotonic
2023-06-13T16:33:43.268277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 62
8.5%
3 62
8.5%
5 62
8.5%
7 62
8.5%
8 62
8.5%
10 62
8.5%
12 62
8.5%
4 60
8.2%
6 60
8.2%
9 60
8.2%
Other values (2) 116
15.9%
ValueCountFrequency (%)
1 62
8.5%
2 56
7.7%
3 62
8.5%
4 60
8.2%
5 62
8.5%
6 60
8.2%
7 62
8.5%
8 62
8.5%
9 60
8.2%
10 62
8.5%
ValueCountFrequency (%)
12 62
8.5%
11 60
8.2%
10 62
8.5%
9 60
8.2%
8 62
8.5%
7 62
8.5%
6 60
8.2%
5 62
8.5%
4 60
8.2%
3 62
8.5%

holiday
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
0
709 
1
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters730
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 709
97.1%
1 21
 
2.9%

Length

2023-06-13T16:33:43.350251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:33:43.503477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 709
97.1%
1 21
 
2.9%

Most occurring characters

ValueCountFrequency (%)
0 709
97.1%
1 21
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 730
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 709
97.1%
1 21
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 730
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 709
97.1%
1 21
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 709
97.1%
1 21
 
2.9%

weekday
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9972603
Minimum0
Maximum6
Zeros105
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-06-13T16:33:43.583125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0061615
Coefficient of variation (CV)0.66933175
Kurtosis-1.2566879
Mean2.9972603
Median Absolute Deviation (MAD)2
Skewness0.0027453495
Sum2188
Variance4.0246838
MonotonicityNot monotonic
2023-06-13T16:33:43.666905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 105
14.4%
0 105
14.4%
1 105
14.4%
2 104
14.2%
4 104
14.2%
5 104
14.2%
3 103
14.1%
ValueCountFrequency (%)
0 105
14.4%
1 105
14.4%
2 104
14.2%
3 103
14.1%
4 104
14.2%
5 104
14.2%
6 105
14.4%
ValueCountFrequency (%)
6 105
14.4%
5 104
14.2%
4 104
14.2%
3 103
14.1%
2 104
14.2%
1 105
14.4%
0 105
14.4%

workingday
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
1
499 
0
231 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters730
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 499
68.4%
0 231
31.6%

Length

2023-06-13T16:33:43.748681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:33:43.861681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1 499
68.4%
0 231
31.6%

Most occurring characters

ValueCountFrequency (%)
1 499
68.4%
0 231
31.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 730
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 499
68.4%
0 231
31.6%

Most occurring scripts

ValueCountFrequency (%)
Common 730
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 499
68.4%
0 231
31.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 499
68.4%
0 231
31.6%

weathersit
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
1
463 
2
246 
3
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters730
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 463
63.4%
2 246
33.7%
3 21
 
2.9%

Length

2023-06-13T16:33:43.943274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-13T16:33:44.025754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1 463
63.4%
2 246
33.7%
3 21
 
2.9%

Most occurring characters

ValueCountFrequency (%)
1 463
63.4%
2 246
33.7%
3 21
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 730
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 463
63.4%
2 246
33.7%
3 21
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 730
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 463
63.4%
2 246
33.7%
3 21
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 463
63.4%
2 246
33.7%
3 21
 
2.9%

temp
Real number (ℝ)

Distinct498
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.319259
Minimum2.4243464
Maximum35.328347
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-06-13T16:33:44.138289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.4243464
5-th percentile8.7470241
Q113.811885
median20.465826
Q326.880615
95-th percentile31.52046
Maximum35.328347
Range32.904001
Interquartile range (IQR)13.068729

Descriptive statistics

Standard deviation7.5067289
Coefficient of variation (CV)0.3694391
Kurtosis-1.1183052
Mean20.319259
Median Absolute Deviation (MAD)6.474597
Skewness-0.057187486
Sum14833.059
Variance56.350979
MonotonicityNot monotonic
2023-06-13T16:33:44.282191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.035 5
 
0.7%
10.899153 5
 
0.7%
27.88 4
 
0.5%
28.563347 4
 
0.5%
23.130847 4
 
0.5%
26.615847 4
 
0.5%
17.9375 4
 
0.5%
19.850847 4
 
0.5%
29.144153 4
 
0.5%
13.085847 3
 
0.4%
Other values (488) 689
94.4%
ValueCountFrequency (%)
2.4243464 1
0.1%
3.9573897 1
0.1%
3.9930433 1
0.1%
4.4075 1
0.1%
5.2275 1
0.1%
5.526103 1
0.1%
5.671653 1
0.1%
5.918268 1
0.1%
6.15 1
0.1%
6.184153 1
0.1%
ValueCountFrequency (%)
35.328347 1
0.1%
34.815847 1
0.1%
34.781653 1
0.1%
34.371653 1
0.1%
34.200847 1
0.1%
34.03 1
0.1%
33.961653 1
0.1%
33.9275 1
0.1%
33.7225 1
0.1%
33.551653 1
0.1%

atemp
Real number (ℝ)

Distinct689
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.726322
Minimum3.95348
Maximum42.0448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-06-13T16:33:44.392684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.95348
5-th percentile11.030712
Q116.889713
median24.368225
Q330.445775
95-th percentile35.749975
Maximum42.0448
Range38.09132
Interquartile range (IQR)13.556062

Descriptive statistics

Standard deviation8.1503078
Coefficient of variation (CV)0.34351333
Kurtosis-0.98451321
Mean23.726322
Median Absolute Deviation (MAD)6.7874
Skewness-0.13370861
Sum17320.215
Variance66.427517
MonotonicityNot monotonic
2023-06-13T16:33:44.579127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.7344 4
 
0.5%
18.78105 3
 
0.4%
31.8504 3
 
0.4%
28.59875 2
 
0.3%
23.32625 2
 
0.3%
30.3981 2
 
0.3%
32.7021 2
 
0.3%
16.2875 2
 
0.3%
29.7673 2
 
0.3%
19.9175 2
 
0.3%
Other values (679) 706
96.7%
ValueCountFrequency (%)
3.95348 1
0.1%
4.941955 1
0.1%
5.0829 1
0.1%
5.80875 1
0.1%
5.8965 1
0.1%
5.96685 1
0.1%
6.31375 1
0.1%
7.21415 1
0.1%
7.4774 1
0.1%
7.54415 1
0.1%
ValueCountFrequency (%)
42.0448 1
0.1%
41.31855 1
0.1%
40.24565 1
0.1%
40.21435 1
0.1%
39.74145 1
0.1%
39.5198 1
0.1%
39.33065 1
0.1%
39.29835 1
0.1%
38.06835 1
0.1%
37.87895 1
0.1%

hum
Real number (ℝ)

Distinct594
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.765175
Minimum0
Maximum97.25
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-06-13T16:33:44.763009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.741735
Q152
median62.625
Q372.989575
95-th percentile86.868735
Maximum97.25
Range97.25
Interquartile range (IQR)20.989575

Descriptive statistics

Standard deviation14.237589
Coefficient of variation (CV)0.22683899
Kurtosis-0.059830074
Mean62.765175
Median Absolute Deviation (MAD)10.45835
Skewness-0.067475863
Sum45818.578
Variance202.70894
MonotonicityNot monotonic
2023-06-13T16:33:44.937044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.3333 4
 
0.5%
63.0833 3
 
0.4%
55.2083 3
 
0.4%
60.5 3
 
0.4%
56.8333 3
 
0.4%
69 3
 
0.4%
59 3
 
0.4%
69.7083 3
 
0.4%
57 3
 
0.4%
48.3333 3
 
0.4%
Other values (584) 699
95.8%
ValueCountFrequency (%)
0 1
0.1%
18.7917 1
0.1%
25.4167 1
0.1%
27.5833 1
0.1%
29 1
0.1%
30.2174 1
0.1%
30.5 1
0.1%
31.125 1
0.1%
31.4167 1
0.1%
31.4348 1
0.1%
ValueCountFrequency (%)
97.25 1
0.1%
97.0417 1
0.1%
96.25 1
0.1%
94.9583 1
0.1%
94.8261 1
0.1%
93.9565 1
0.1%
93 1
0.1%
92.9167 1
0.1%
92.5 1
0.1%
92.25 1
0.1%

windspeed
Real number (ℝ)

Distinct649
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.76362
Minimum1.5002439
Maximum34.000021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-06-13T16:33:45.100847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.5002439
5-th percentile5.3260521
Q19.04165
median12.125325
Q315.625589
95-th percentile22.999988
Maximum34.000021
Range32.499777
Interquartile range (IQR)6.5839393

Descriptive statistics

Standard deviation5.1958407
Coefficient of variation (CV)0.40708207
Kurtosis0.40590905
Mean12.76362
Median Absolute Deviation (MAD)3.291643
Skewness0.67631404
Sum9317.4423
Variance26.996761
MonotonicityNot monotonic
2023-06-13T16:33:45.274809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.041918 3
 
0.4%
11.166689 3
 
0.4%
11.250104 3
 
0.4%
15.333486 3
 
0.4%
7.959064 3
 
0.4%
7.12545 3
 
0.4%
7.4169 3
 
0.4%
9.166739 3
 
0.4%
10.042161 3
 
0.4%
14.791925 2
 
0.3%
Other values (639) 701
96.0%
ValueCountFrequency (%)
1.5002439 1
0.1%
2.8343814 1
0.1%
3.0420814 1
0.1%
3.0423561 1
0.1%
3.12555 1
0.1%
3.167425 1
0.1%
3.3754064 1
0.1%
3.5423436 1
0.1%
3.565271 1
0.1%
3.834075 1
0.1%
ValueCountFrequency (%)
34.000021 1
0.1%
29.584721 1
0.1%
28.292425 1
0.1%
28.250014 1
0.1%
27.999836 1
0.1%
27.833743 1
0.1%
27.7916 1
0.1%
27.417204 1
0.1%
27.292182 1
0.1%
26.666536 1
0.1%

casual
Real number (ℝ)

Distinct605
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean849.24932
Minimum2
Maximum3410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-06-13T16:33:45.438568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile88.45
Q1316.25
median717
Q31096.5
95-th percentile2355
Maximum3410
Range3408
Interquartile range (IQR)780.25

Descriptive statistics

Standard deviation686.47987
Coefficient of variation (CV)0.80833727
Kurtosis1.3213424
Mean849.24932
Median Absolute Deviation (MAD)400
Skewness1.2663277
Sum619952
Variance471254.62
MonotonicityNot monotonic
2023-06-13T16:33:45.602602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 4
 
0.5%
968 4
 
0.5%
639 3
 
0.4%
163 3
 
0.4%
775 3
 
0.4%
244 3
 
0.4%
140 3
 
0.4%
123 3
 
0.4%
653 3
 
0.4%
692 2
 
0.3%
Other values (595) 699
95.8%
ValueCountFrequency (%)
2 1
0.1%
9 2
0.3%
15 1
0.1%
25 1
0.1%
34 1
0.1%
38 2
0.3%
41 1
0.1%
42 1
0.1%
43 1
0.1%
46 1
0.1%
ValueCountFrequency (%)
3410 1
0.1%
3283 1
0.1%
3252 1
0.1%
3160 1
0.1%
3155 1
0.1%
3065 1
0.1%
3031 1
0.1%
2963 1
0.1%
2855 1
0.1%
2846 1
0.1%

registered
Real number (ℝ)

Distinct678
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3658.7575
Minimum20
Maximum6946
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-06-13T16:33:45.735484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile1177.05
Q12502.25
median3664.5
Q34783.25
95-th percentile6280.55
Maximum6946
Range6926
Interquartile range (IQR)2281

Descriptive statistics

Standard deviation1559.7587
Coefficient of variation (CV)0.42630831
Kurtosis-0.71024959
Mean3658.7575
Median Absolute Deviation (MAD)1157
Skewness0.041211058
Sum2670893
Variance2432847.3
MonotonicityNot monotonic
2023-06-13T16:33:45.868265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4841 3
 
0.4%
6248 3
 
0.4%
1707 3
 
0.4%
3461 2
 
0.3%
2713 2
 
0.3%
1628 2
 
0.3%
4429 2
 
0.3%
1730 2
 
0.3%
2549 2
 
0.3%
4232 2
 
0.3%
Other values (668) 707
96.8%
ValueCountFrequency (%)
20 1
0.1%
416 1
0.1%
432 1
0.1%
451 1
0.1%
472 1
0.1%
491 1
0.1%
570 1
0.1%
573 1
0.1%
577 1
0.1%
654 1
0.1%
ValueCountFrequency (%)
6946 1
0.1%
6917 1
0.1%
6911 1
0.1%
6898 1
0.1%
6844 1
0.1%
6820 1
0.1%
6803 1
0.1%
6790 1
0.1%
6781 1
0.1%
6750 1
0.1%

cnt
Real number (ℝ)

Distinct695
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4508.0068
Minimum22
Maximum8714
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2023-06-13T16:33:46.001114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile1330
Q13169.75
median4548.5
Q35966
95-th percentile7576.4
Maximum8714
Range8692
Interquartile range (IQR)2796.25

Descriptive statistics

Standard deviation1936.0116
Coefficient of variation (CV)0.42946067
Kurtosis-0.80807954
Mean4508.0068
Median Absolute Deviation (MAD)1397.5
Skewness-0.049580605
Sum3290845
Variance3748141.1
MonotonicityNot monotonic
2023-06-13T16:33:46.154286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5409 2
 
0.3%
2424 2
 
0.3%
5698 2
 
0.3%
4459 2
 
0.3%
5119 2
 
0.3%
1096 2
 
0.3%
1685 2
 
0.3%
4401 2
 
0.3%
6883 2
 
0.3%
6591 2
 
0.3%
Other values (685) 710
97.3%
ValueCountFrequency (%)
22 1
0.1%
431 1
0.1%
441 1
0.1%
506 1
0.1%
605 1
0.1%
623 1
0.1%
627 1
0.1%
683 1
0.1%
705 1
0.1%
754 1
0.1%
ValueCountFrequency (%)
8714 1
0.1%
8555 1
0.1%
8395 1
0.1%
8362 1
0.1%
8294 1
0.1%
8227 1
0.1%
8173 1
0.1%
8167 1
0.1%
8156 1
0.1%
8120 1
0.1%

Interactions

2023-06-13T16:33:40.175472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:29.831158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:30.586497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:31.331441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:32.236182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:33.404186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:34.776274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:36.058453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:37.554761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:38.843383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:40.306738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:29.915323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:30.668496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:31.407377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:32.323507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:33.514404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:34.902184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:36.363828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:37.715857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:38.974493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:40.440875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:29.988109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:30.736719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:31.469869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:32.401090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:33.638819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:35.052345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:36.522790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:37.810292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:39.113604image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:40.538250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:30.067717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:30.807238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:31.551098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:32.482975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:33.779580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:35.164453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:36.665851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:37.933375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:39.238214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:40.653957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:30.139947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:30.878106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:31.625202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:32.589272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:33.897490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:35.289532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:36.774650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:38.045333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:39.366969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:40.792943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:30.217585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:30.955754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:31.697167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:32.699709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:34.026931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:35.455601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:36.922185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:38.163130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:39.553960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:40.897322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:30.290144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:31.032163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:31.772904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:32.825525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:34.222812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:35.557949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:37.043013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:38.320635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:39.701195image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:41.039225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:30.367148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:31.108899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:31.935530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:32.996256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:34.356227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:35.686910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:37.176775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:38.463943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:39.830515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:41.197090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:30.448225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:31.186529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:32.009666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:33.143327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:34.493736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:35.829915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:37.288944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:38.587379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:39.937066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:41.315620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:30.523910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:31.262276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:32.134866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:33.266724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:34.674545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:35.956767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:37.419786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:38.754032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-06-13T16:33:40.034173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-06-13T16:33:46.317865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
instantmnthweekdaytempatemphumwindspeedcasualregisteredcntseasonyrholidayworkingdayweathersit
instant1.0000.498-0.0000.1420.1420.010-0.1300.3140.6650.6310.7990.9940.0000.0000.122
mnth0.4981.0000.0090.2060.2070.216-0.2070.1820.2810.2700.8840.0000.0000.0000.103
weekday-0.0000.0091.000-0.004-0.013-0.0540.0130.0400.0580.0650.0000.0000.2680.9350.042
temp0.1420.206-0.0041.0000.9930.132-0.1470.6670.5300.6210.5720.1120.0000.0440.148
atemp0.1420.207-0.0130.9931.0000.141-0.1690.6670.5310.6220.5770.0800.0220.0890.169
hum0.0100.216-0.0540.1320.1411.000-0.239-0.068-0.091-0.0960.1510.1520.0000.0560.550
windspeed-0.130-0.2070.013-0.147-0.169-0.2391.000-0.181-0.203-0.2170.1900.0620.0000.0460.111
casual0.3140.1820.0400.6670.667-0.068-0.1811.0000.5220.7530.3730.3180.0350.5710.221
registered0.6650.2810.0580.5300.531-0.091-0.2030.5221.0000.9400.3600.6490.0690.3760.308
cnt0.6310.2700.0650.6210.622-0.096-0.2170.7530.9401.0000.3790.6420.0920.1530.334
season0.7990.8840.0000.5720.5770.1510.1900.3730.3600.3791.0000.0000.0000.0000.077
yr0.9940.0000.0000.1120.0800.1520.0620.3180.6490.6420.0001.0000.0000.0000.054
holiday0.0000.0000.2680.0000.0220.0000.0000.0350.0690.0920.0000.0001.0000.2410.000
workingday0.0000.0000.9350.0440.0890.0560.0460.5710.3760.1530.0000.0000.2411.0000.030
weathersit0.1220.1030.0420.1480.1690.5500.1110.2210.3080.3340.0770.0540.0000.0301.000

Missing values

2023-06-13T16:33:41.517889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-13T16:33:41.787255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

instantdtedayseasonyrmnthholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
0101-01-2018101060214.11084718.1812580.583310.749882331654985
1202-01-2018101000214.90259817.6869569.608716.652113131670801
2303-01-201810101118.0509249.4702543.727316.63670312012291349
3404-01-201810102118.20000010.6061059.043510.73983210814541562
4505-01-201810103119.30523711.4635043.695712.5223008215181600
5606-01-201810104118.37826811.6604551.82616.0008688815181606
6707-01-201810105128.05740210.4419549.869611.30464214813621510
7808-01-201810106026.7650008.1127053.583317.87586868891959
8909-01-201810100015.6716535.8087543.416724.25065054768822
91010-01-201810101116.1841537.5444048.291714.9588894112801321
instantdtedayseasonyrmnthholidayweekdayworkingdayweathersittempatemphumwindspeedcasualregisteredcnt
72072122-12-20191112060110.89915311.8056544.125027.29218220515441749
72172223-12-20191112000110.07915312.9735551.54178.91656140813791787
72272324-12-2019111201129.48346412.9450079.13045.174437174746920
72372425-12-20191112120211.94346414.7232573.478311.3046424405731013
72472526-12-2019111203139.97665311.0166582.333321.2085829432441
72572627-12-20191112041210.42084711.3321065.291723.45891124718672114
72672728-12-20191112051210.38665312.7523059.000010.41655764424513095
72772829-12-20191112060210.38665312.1200075.29178.33366115911821341
72872930-12-20191112000110.48915311.5850048.333323.50051836414321796
72973031-12-2019111201128.84915311.1743557.750010.37468243922902729